4 papers
Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching
Jiacheng Cheng, Xu Zhang, Guanghui Qiu +3
Federated Learning (FL) enables collaborative training across decentralized data, but faces key challenges of bidirectional communication overhead and client-side data heterogeneit…
Exploring Dynamic Properties of Backdoor Training Through Information Bottleneck
Xinyu Liu, Xu Zhang, Can Chen +1
Understanding how backdoor data influences neural network training dynamics remains a complex and underexplored challenge. In this paper, we present a rigorous analysis of the impa…
Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach
Xu Zhang, Kaidi Xu, Ziqing Hu +1
Mixture of Experts (MoE) have shown remarkable success in leveraging specialized expert networks for complex machine learning tasks. However, their susceptibility to adversarial at…
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer Pruning
Qingsong Lv, Jiasheng Sun, Sheng Zhou +6
To reduce computational overhead while maintaining model performance, model pruning techniques have been proposed. Among these, structured pruning, which removes entire convolution…